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• W2271339716 abstract "Community detection in graphs is widely used in social and biological networks, and the stochastic block model is a powerful probabilistic tool for describing graphs with community structures. However, in the era of “big data”, traditional inference algorithms for such a model are increasingly limi ted due to their high time complexity and poor scalability. In this paper, we propose a multi-stage maximum likelihood approach to recover the latent parameters of the stochastic block model, in time linear with respect to the number of edges. We also propose a parallel algorithm based on message passing. Our algorithm can overlap communication and computation, providing speedup without compromising accuracy as the number of processors grows. For example, to process a real-world graph with about 1.3 million nodes and 10 million edges, our algorithm requires about 6 seconds on 64 cores of a contemporary commodity Linux cluster. Experiments demonstrate that the algorithm can produce high quality results on both benchmark and real-world graphs. An example of finding more meaningful communities is illustrated consequently in comparison with a popular modularity maximization algorithm." @default.
• W2271339716 created "2016-06-24" @default.
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• W2271339716 date "2017-11-15" @default.
• W2271339716 modified "2023-10-16" @default.
• W2271339716 title "A scalable community detection algorithm for large graphs using stochastic block models" @default.
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• W2271339716 doi "https://doi.org/10.3233/ida-163156" @default.
• W2271339716 hasPublicationYear "2017" @default.
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